Measuring the Impact of Nonignorable Non-Response in Panel Data with Non-Monotone Missingness

Abstract

The analysis of panel data with non-monotone nonresponse often relies on the critical and untestable assumption of ignorable missingness. It is important to assess the consequences of departures from the ignorability assumption. Non-monotone nonresponse, however, can often make such sensitivity analysis infeasible because the likelihood functions for alternative models involve high-dimensional and difficult-to-evaluate integrals with respect to missing outcomes. We develop an extension of the local sensitivity method that overcomes computational difficulty and completely avoids fitting alternative models and evaluating these high-dimensional integrals. The proposed method is applicable to a wide range of panel outcomes. We apply the method to a Smoking Trend dataset where we relax the standard ignorability assumption and evaluate how smoking trend estimates in different groups of U.S. young adults are affected by alternative assumptions about the missing-data mechanism. The main finding is that the standard estimate in the black-male group is sensitive to nonignorable missingness but those in other groups are reasonably robust.

Keywords: Generalized Least Square,Generalized Linear Mixed Model, Nonignorability, Sensitivity Analysis

JEL Classification: C01, C23, C33